arXiv subjects

Kellan Colburn

Publications and source records attributed to Kellan Colburn.

3 recordsLinked to original sources

arXiv2024

The quest for CMB signatures of Conformal Cyclic Cosmology

Circles of low-variance and Hawking points in the Cosmic Microwave Background (CMB), resulting from black hole mergers and black hole evaporation, respectively, in a previous cycle of the universe, have been predicted as possible evidence for the Conformal Cyclic Cosmology model (CCC) introduced by R. Penrose. We present a high-resolution search for such low-variance circles in the Planck and WMAP CMB data, and introduce HawkingNet, our machine learning open-source software based on a ResNet18 algorithm, to search for Hawking points in the CMB. We find that spots consisting of a few unusually bright (high-temperature) or dark (low-temperature) pixels, erroneously lead to regions with many low-variance circles, and consequently sets of near-concentric low-variance circles, when applying the search criteria used in previous work [V.G. Gurzadyan, R. Penrose]. After removing those spots from the data, no statistically significant low-variance circles can be found. Concerning Hawking points, also no statistically significant evidence is found when using a Gaussian temperature amplitude model over 1 degree opening angle and after accounting for spots of unusual brightness. That the unusual spots in the data are themselves remnants of Hawking points is not supported by low-variance and/or low-temperature circles around them. The absence of such statistically-significant distinct features in the currently available CMB data does not disprove the CCC model, but implies that higher resolution CMB data and/or refined CCC based predictions are needed to pursue the search for CCC signatures further

astro-ph.CO↗

arXiv2023

OH absorption in on-chip high-Q resonators

Thermal silica is a common dielectric used in all silicon-photonic circuits. And bound hydroxyl ions (Si-OH) can provide a significant component of optical loss in this material on account of the wet nature of the thermal oxidation process. A convenient way to quantify this loss relative to other mechanisms is through OH-absorption at 1380 nm. Here, using ultra-high-Q thermal-silica wedge microresonators, the OH absorption loss peak is measured and distinguished from the scattering loss base line over a wavelength range from 680 nm to 1550 nm. Record-high on-chip resonator Q factors are observed for near-visible and visible wavelengths, and the absorption limited Q factor is as high as 8 billion in the telecom band. OH ion content level around 2.4 ppm (weight) is inferred from both Q measurements and by Secondary Ion Mass Spectroscopy (SIMS) depth profiling.

physics.optics↗

arXiv2020

Model Optimization for Deep Space Exploration via Simulators and Deep Learning

Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep learning using neural networks in order to automate the detection of astronomical bodies for future exploration missions, such as missions to search for signatures or suitability of life. The ability to acquire images, analyze them, and send back those that are important, as determined by the deep learning algorithm, is critical in bandwidth-limited applications. Our previous foundational work solidified the concept of using simulator images and deep learning in order to detect planets. Optimization of this process is of vital importance, as even a small loss in accuracy might be the difference between capturing and completely missing a possibly-habitable nearby planet. Through computer vision, deep learning, and simulators, we introduce methods that optimize the detection of exoplanets. We show that maximum achieved accuracy can hit above 98% for multiple model architectures, even with a relatively small training set.

astro-ph.IM↗